Estimating regression models in which the dependent variable is based on estimates
Estimating regression models in which the dependent variable is based on estimates
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DOI：
10.1093/pan/mpi026
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发表时间：
2005-09-01
影响因子：
5.4
通讯作者：
Linzer, DA
中科院分区：
文献类型：
--
作者：
Lewis, JB;Linzer, DA
研究人员经常使用从辅助数据集估计的数量作为因变量。例如，估计因变量(EDV)模型出现在以县或州为分析单位、因变量为估计平均值、比例或回归系数的研究中。拟合EDV模型的学者们普遍认识到，因变量上观测值的抽样方差的变化会导致异方差。我们表明，对于这个问题，最常见的方法是加权最小二乘法，这通常会导致估计效率低下和低估标准误差。在许多情况下，具有White或Efron异方差一致标准误差的OLS会产生更好的结果。我们还提出了两种简单的替代FGLS方法，这两种方法更有效，并产生一致的标准误差估计。最后，我们应用不同的替代估计量来复制科恩(2004)对总统批准的跨国研究。
Researchers often use as dependent variables quantities estimated from auxiliary data sets. Estimated dependent variable (EDV) models arise, for example, in studies where counties or states are the units of analysis and the dependent variable is an estimated mean, proportion, or regression coefficient. Scholars fitting EDV models have generally recognized that variation in the sampling variance of the observations on the dependent variable will induce heteroscedasticity. We show that the most common approach to this problem, weighted least squares, will usually lead to inefficient estimates and underestimated standard errors. In many cases, OLS with White's or Efron heteroscedastic consistent standard errors yields better results. We also suggest two simple alternative FGLS approaches that are more efficient and yield consistent standard error estimates. Finally, we apply the various alternative estimators to a replication of Cohen's (2004) cross-national study of presidential approval.